Josh Dillon, Last Revised January 2022
This notebook examines an individual antenna's performance over a whole season. This notebook parses information from each nightly rtp_summarynotebook (as saved to .csvs) and builds a table describing antenna performance. It also reproduces per-antenna plots from each auto_metrics notebook pertinent to the specific antenna.
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "004"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder
# Use environment variables to figure out path to the csvs and auto_metrics
antenna = str(int(os.environ["ANTENNA"]))
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')
antenna = "20" csv_folder = "/home/obs/src/H6C_Notebooks/_rtp_summary_" auto_metrics_folder = "/home/obs/src/H6C_Notebooks/auto_metrics_inspect"
display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))
import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna
# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, 'rtp_summary_table*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')
Found 33 csvs in /home/obs/src/H6C_Notebooks/_rtp_summary_ Found 33 auto_metrics notebooks in /home/obs/src/H6C_Notebooks/auto_metrics_inspect
# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0
def jd_to_summary_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'
def jd_to_auto_metrics_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'
this_antenna = None
jds = []
# parse information about antennas and nodes
for csv in csvs:
df = pd.read_csv(csv)
for n in range(len(df)):
# Add this day to the antenna
row = df.loc[n]
if isinstance(row['Ant'], str) and '<a href' in row['Ant']:
antnum = int(row['Ant'].split('</a>')[0].split('>')[-1]) # it's a link, extract antnum
else:
antnum = int(row['Ant'])
if antnum != int(antenna):
continue
if np.issubdtype(type(row['Node']), np.integer):
row['Node'] = str(row['Node'])
if type(row['Node']) == str and row['Node'].isnumeric():
row['Node'] = 'N' + ('0' if len(row['Node']) == 1 else '') + row['Node']
if this_antenna is None:
this_antenna = Antenna(row['Ant'], row['Node'])
jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
jds.append(jd)
this_antenna.add_day(jd, row)
break
# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]
df = pd.DataFrame(to_show)
# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
df[col] = bar_cols[col]
z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
df[col] = z_score_cols[col]
ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
df[col] = ant_metrics_cols[col]
redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]
for col in redcal_cols:
df[col] = redcal_cols[col]
# style dataframe
table = df.style.hide_index()\
.applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
.background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
.background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
.applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
.format({col: '{:,.4f}'.format for col in z_score_cols}) \
.format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
.format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
.set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])
This table reproduces each night's row for this antenna from the RTP Summary notebooks. For more info on the columns, see those notebooks, linked in the JD column.
display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))
| JDs | A Priori Status | Auto Metrics Flags | Dead Fraction in Ant Metrics (Jee) | Dead Fraction in Ant Metrics (Jnn) | Crossed Fraction in Ant Metrics | Flag Fraction Before Redcal | Flagged By Redcal chi^2 Fraction | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | Average Dead Ant Metric (Jee) | Average Dead Ant Metric (Jnn) | Average Crossed Ant Metric | Median chi^2 Per Antenna (Jee) | Median chi^2 Per Antenna (Jnn) |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2460016 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 3.169317 | -1.487663 | 2.226109 | -0.970064 | 1.036061 | -0.001359 | 2.797659 | -0.103334 | 0.5638 | 0.5902 | 0.3505 | nan | nan |
| 2460015 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 1.028177 | -1.299346 | 2.303572 | -0.904247 | 1.831036 | 0.415470 | 1.935856 | -0.379235 | 0.5785 | 0.5983 | 0.3489 | nan | nan |
| 2460014 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.139161 | -1.156392 | 1.847308 | -0.804273 | 0.282505 | 0.774778 | 1.700531 | 0.054372 | 0.5512 | 0.5764 | 0.3505 | nan | nan |
| 2460013 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.030267 | -1.304881 | 2.330994 | -0.905313 | 1.454380 | -0.282233 | 1.848064 | -0.222058 | 0.5756 | 0.5989 | 0.3565 | nan | nan |
| 2460012 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.228562 | -1.157513 | 2.132590 | -0.961594 | 2.102561 | -0.067807 | 1.152720 | -0.697442 | 0.5771 | 0.5993 | 0.3513 | nan | nan |
| 2460011 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 231.720824 | 231.712148 | inf | inf | 5307.797796 | 5228.739270 | 10536.546416 | 10301.179798 | nan | nan | nan | nan | nan |
| 2460010 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | nan | nan | inf | inf | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 2460009 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | nan | nan | inf | inf | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 2460008 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 278.949234 | 279.865119 | inf | inf | 3300.650138 | 3300.664938 | 3780.064722 | 3808.632031 | nan | nan | nan | nan | nan |
| 2460007 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | nan | nan | inf | inf | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 2459999 | digital_ok | 0.00% | 99.83% | 99.83% | 0.00% | - | - | nan | nan | nan | nan | nan | nan | nan | nan | 0.1636 | 0.1645 | 0.1197 | nan | nan |
| 2459998 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.013455 | -1.260853 | 1.732733 | -0.781774 | 1.392046 | 0.317015 | 1.777497 | -0.305909 | 0.6011 | 0.6192 | 0.3790 | nan | nan |
| 2459997 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.279352 | -1.473949 | 1.747044 | -0.631793 | 0.506451 | -0.058759 | 2.277445 | -0.261524 | 0.6152 | 0.6345 | 0.3815 | nan | nan |
| 2459996 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 0.546354 | -1.416494 | 1.083285 | -0.576603 | 6.168828 | -0.644037 | 0.808651 | -0.330056 | 0.6235 | 0.6392 | 0.3913 | nan | nan |
| 2459995 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 0.440407 | -1.566178 | 1.303400 | -0.985981 | 8.765226 | -0.320614 | 0.916142 | -0.558013 | 0.6148 | 0.6350 | 0.3794 | nan | nan |
| 2459994 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.351503 | -1.445357 | 1.830533 | -0.772512 | 0.928512 | 0.184834 | 0.729243 | -0.624695 | 0.6067 | 0.6271 | 0.3774 | nan | nan |
| 2459993 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.284045 | -1.608784 | 1.428042 | -0.931258 | 2.232637 | 0.499541 | 1.847444 | -0.471310 | 0.6028 | 0.6352 | 0.3862 | nan | nan |
| 2459991 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 0.515609 | -1.667058 | 1.337756 | -0.939209 | 6.280610 | 0.998124 | 1.263694 | -0.581911 | 0.6134 | 0.6280 | 0.3823 | nan | nan |
| 2459990 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.441123 | -1.338505 | 1.823472 | -0.953929 | 1.868566 | 0.629300 | 3.271557 | -0.561501 | 0.6106 | 0.6289 | 0.3823 | nan | nan |
| 2459989 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.378899 | -1.494769 | 1.684125 | -0.580996 | 0.362157 | 0.547117 | 1.319377 | -0.623366 | 0.6064 | 0.6274 | 0.3840 | nan | nan |
| 2459988 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.341666 | -1.588027 | 1.912273 | -1.092796 | 0.042383 | 0.182352 | 1.323484 | -0.743746 | 0.6068 | 0.6254 | 0.3762 | nan | nan |
| 2459987 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 0.397110 | -1.532272 | 1.058951 | -0.762239 | 7.784754 | -0.351013 | 0.844914 | -0.754430 | 0.6172 | 0.6350 | 0.3729 | nan | nan |
| 2459986 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 0.288845 | -1.652357 | 1.227251 | -1.070325 | 6.626431 | -0.166627 | 1.807197 | -0.279954 | 0.6322 | 0.6507 | 0.3422 | nan | nan |
| 2459985 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.473130 | -1.591510 | 0.487981 | -0.900375 | 1.845351 | -0.204672 | 2.315851 | -0.197746 | 0.6169 | 0.6316 | 0.3804 | nan | nan |
| 2459984 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.253564 | -1.274419 | 0.607421 | -0.747898 | 0.769561 | 0.110581 | 0.223052 | -0.727750 | 0.6350 | 0.6445 | 0.3568 | nan | nan |
| 2459983 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.228093 | -1.009774 | 2.560038 | -1.020399 | -0.154081 | 0.351181 | 2.029457 | -0.322500 | 0.6418 | 0.6725 | 0.3211 | nan | nan |
| 2459982 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.508343 | 0.197963 | 2.202690 | -0.620876 | 0.106327 | 0.595692 | 2.009579 | -0.129483 | 0.6832 | 0.6906 | 0.2981 | nan | nan |
| 2459981 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.175074 | -1.105296 | 2.655419 | -1.262204 | 0.303508 | 0.831261 | 3.374116 | -0.245788 | 0.6100 | 0.6380 | 0.3834 | nan | nan |
| 2459980 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.791844 | -0.968581 | 2.253589 | -1.053695 | 0.071211 | 0.680046 | 3.543993 | -0.304094 | 0.6525 | 0.6726 | 0.3113 | nan | nan |
| 2459979 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.105573 | -1.266949 | 2.069077 | -1.041651 | 0.255545 | 1.238962 | 1.190757 | -0.795385 | 0.6048 | 0.6358 | 0.3859 | nan | nan |
| 2459978 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.675882 | -1.265792 | 2.299287 | -1.145400 | 0.236948 | 0.760070 | 2.650998 | -0.784742 | 0.6025 | 0.6329 | 0.3916 | nan | nan |
| 2459977 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.985280 | -1.120333 | 2.202615 | -1.061054 | -0.148865 | 1.039964 | 0.646182 | -1.021778 | 0.5658 | 0.5974 | 0.3508 | nan | nan |
| 2459976 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.139986 | -1.152787 | 2.435276 | -1.134847 | -0.112951 | 0.892407 | 1.201615 | -0.784448 | 0.6146 | 0.6411 | 0.3834 | nan | nan |
auto_metrics notebooks.¶htmls_to_display = []
for am_html in auto_metric_htmls:
html_to_display = ''
# read html into a list of lines
with open(am_html) as f:
lines = f.readlines()
# find section with this antenna's metric plots and add to html_to_display
jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
try:
section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
except ValueError:
continue
html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
for line in lines[section_start_line + 1:]:
html_to_display += line
if '<hr' in line:
htmls_to_display.append(html_to_display)
break
These figures are reproduced from auto_metrics notebooks. For more info on the specific plots and metrics, see those notebooks (linked at the JD). The most recent 100 days (at most) are shown.
for i, html_to_display in enumerate(htmls_to_display):
if i == 100:
break
display(HTML(html_to_display))
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 20 | N02 | digital_ok | ee Shape | 3.169317 | -1.487663 | 3.169317 | -0.970064 | 2.226109 | -0.001359 | 1.036061 | -0.103334 | 2.797659 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 20 | N02 | digital_ok | ee Power | 2.303572 | -1.299346 | 1.028177 | -0.904247 | 2.303572 | 0.415470 | 1.831036 | -0.379235 | 1.935856 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 20 | N02 | digital_ok | ee Power | 1.847308 | 0.139161 | -1.156392 | 1.847308 | -0.804273 | 0.282505 | 0.774778 | 1.700531 | 0.054372 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 20 | N02 | digital_ok | ee Power | 2.330994 | -0.030267 | -1.304881 | 2.330994 | -0.905313 | 1.454380 | -0.282233 | 1.848064 | -0.222058 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 20 | N02 | digital_ok | ee Power | 2.132590 | 0.228562 | -1.157513 | 2.132590 | -0.961594 | 2.102561 | -0.067807 | 1.152720 | -0.697442 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 20 | N02 | digital_ok | ee Power | inf | 231.720824 | 231.712148 | inf | inf | 5307.797796 | 5228.739270 | 10536.546416 | 10301.179798 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 20 | N02 | digital_ok | ee Shape | nan | nan | nan | inf | inf | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 20 | N02 | digital_ok | ee Shape | nan | nan | nan | inf | inf | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 20 | N02 | digital_ok | nn Power | inf | 279.865119 | 278.949234 | inf | inf | 3300.664938 | 3300.650138 | 3808.632031 | 3780.064722 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 20 | N02 | digital_ok | ee Shape | nan | nan | nan | inf | inf | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 20 | N02 | digital_ok | nn Shape | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 20 | N02 | digital_ok | ee Temporal Discontinuties | 1.777497 | -0.013455 | -1.260853 | 1.732733 | -0.781774 | 1.392046 | 0.317015 | 1.777497 | -0.305909 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 20 | N02 | digital_ok | ee Temporal Discontinuties | 2.277445 | -0.279352 | -1.473949 | 1.747044 | -0.631793 | 0.506451 | -0.058759 | 2.277445 | -0.261524 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 20 | N02 | digital_ok | ee Temporal Variability | 6.168828 | 0.546354 | -1.416494 | 1.083285 | -0.576603 | 6.168828 | -0.644037 | 0.808651 | -0.330056 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 20 | N02 | digital_ok | ee Temporal Variability | 8.765226 | 0.440407 | -1.566178 | 1.303400 | -0.985981 | 8.765226 | -0.320614 | 0.916142 | -0.558013 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 20 | N02 | digital_ok | ee Power | 1.830533 | 0.351503 | -1.445357 | 1.830533 | -0.772512 | 0.928512 | 0.184834 | 0.729243 | -0.624695 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 20 | N02 | digital_ok | ee Temporal Variability | 2.232637 | 0.284045 | -1.608784 | 1.428042 | -0.931258 | 2.232637 | 0.499541 | 1.847444 | -0.471310 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 20 | N02 | digital_ok | ee Temporal Variability | 6.280610 | 0.515609 | -1.667058 | 1.337756 | -0.939209 | 6.280610 | 0.998124 | 1.263694 | -0.581911 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 20 | N02 | digital_ok | ee Temporal Discontinuties | 3.271557 | -1.338505 | 0.441123 | -0.953929 | 1.823472 | 0.629300 | 1.868566 | -0.561501 | 3.271557 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 20 | N02 | digital_ok | ee Power | 1.684125 | -1.494769 | 0.378899 | -0.580996 | 1.684125 | 0.547117 | 0.362157 | -0.623366 | 1.319377 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 20 | N02 | digital_ok | ee Power | 1.912273 | -1.588027 | 0.341666 | -1.092796 | 1.912273 | 0.182352 | 0.042383 | -0.743746 | 1.323484 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 20 | N02 | digital_ok | ee Temporal Variability | 7.784754 | 0.397110 | -1.532272 | 1.058951 | -0.762239 | 7.784754 | -0.351013 | 0.844914 | -0.754430 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 20 | N02 | digital_ok | ee Temporal Variability | 6.626431 | -1.652357 | 0.288845 | -1.070325 | 1.227251 | -0.166627 | 6.626431 | -0.279954 | 1.807197 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 20 | N02 | digital_ok | ee Temporal Discontinuties | 2.315851 | -1.591510 | 0.473130 | -0.900375 | 0.487981 | -0.204672 | 1.845351 | -0.197746 | 2.315851 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 20 | N02 | digital_ok | ee Temporal Variability | 0.769561 | -0.253564 | -1.274419 | 0.607421 | -0.747898 | 0.769561 | 0.110581 | 0.223052 | -0.727750 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 20 | N02 | digital_ok | ee Power | 2.560038 | 0.228093 | -1.009774 | 2.560038 | -1.020399 | -0.154081 | 0.351181 | 2.029457 | -0.322500 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 20 | N02 | digital_ok | ee Power | 2.202690 | 0.508343 | 0.197963 | 2.202690 | -0.620876 | 0.106327 | 0.595692 | 2.009579 | -0.129483 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 20 | N02 | digital_ok | ee Temporal Discontinuties | 3.374116 | -1.105296 | 0.175074 | -1.262204 | 2.655419 | 0.831261 | 0.303508 | -0.245788 | 3.374116 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 20 | N02 | digital_ok | ee Temporal Discontinuties | 3.543993 | -0.968581 | 0.791844 | -1.053695 | 2.253589 | 0.680046 | 0.071211 | -0.304094 | 3.543993 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 20 | N02 | digital_ok | ee Power | 2.069077 | 0.105573 | -1.266949 | 2.069077 | -1.041651 | 0.255545 | 1.238962 | 1.190757 | -0.795385 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 20 | N02 | digital_ok | ee Temporal Discontinuties | 2.650998 | -1.265792 | 0.675882 | -1.145400 | 2.299287 | 0.760070 | 0.236948 | -0.784742 | 2.650998 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 20 | N02 | digital_ok | ee Power | 2.202615 | 0.985280 | -1.120333 | 2.202615 | -1.061054 | -0.148865 | 1.039964 | 0.646182 | -1.021778 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 20 | N02 | digital_ok | ee Power | 2.435276 | -1.152787 | 0.139986 | -1.134847 | 2.435276 | 0.892407 | -0.112951 | -0.784448 | 1.201615 |